from openai import OpenAI

client = OpenAI()

# Process an entire codebase in a single request
with open("entire_project.py", "r") as f:
    codebase = f.read()

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {
            "role": "system",
            "content": "You are an expert code reviewer."
        },
        {
            "role": "user",
            "content": f"Analyze this entire codebase for security vulnerabilities:\n\n{codebase}"
        }
    ],
    max_tokens=4096
)

import base64
from openai import OpenAI

client = OpenAI()

def analyze_video_frames(video_frames: list[str], query: str) -> str:
    """Analyze multiple video frames with a single API call."""
    
    content = [{"type": "text", "text": query}]
    
    for frame_path in video_frames:
        with open(frame_path, "rb") as f:
            image_data = base64.b64encode(f.read()).decode("utf-8")
        
        content.append({
            "type": "image_url",
            "image_url": {
                "url": f"data:image/jpeg;base64,{image_data}",
                "detail": "high"
            }
        })
    
    response = client.chat.completions.create(
        model="gpt-5",
        messages=[{"role": "user", "content": content}]
    )
    
    return response.choices[0].message.content

# Analyze a video sequence
frames = [f"frame_{i:04d}.jpg" for i in range(0, 300, 10)]
analysis = analyze_video_frames(frames, "Describe the actions occurring in this video sequence")

# GPT-5 automatically uses extended thinking for complex problems
response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {
            "role": "user",
            "content": """
            Given this microservices architecture with 50 services, 
            identify all potential single points of failure and suggest 
            mitigation strategies. Services: [... detailed architecture ...]
            """
        }
    ],
    # Enable extended reasoning mode
    reasoning_effort="high"
)

from openai import OpenAI
import json
from typing import Any

client = OpenAI()

# Define tools for the agent
tools = [
    {
        "type": "function",
        "function": {
            "name": "execute_code",
            "description": "Execute Python code and return the result",
            "parameters": {
                "type": "object",
                "properties": {
                    "code": {
                        "type": "string",
                        "description": "Python code to execute"
                    }
                },
                "required": ["code"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "write_file",
            "description": "Write content to a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "filename": {"type": "string"},
                    "content": {"type": "string"}
                },
                "required": ["filename", "content"]
            }
        }
    }
]

def run_agent(task: str, max_iterations: int = 10) -> str:
    messages = [{"role": "user", "content": task}]
    
    for _ in range(max_iterations):
        response = client.chat.completions.create(
            model="gpt-5",
            messages=messages,
            tools=tools,
            tool_choice="auto"
        )
        
        message = response.choices[0].message
        
        if message.tool_calls is None:
            return message.content
        
        messages.append(message)
        
        for tool_call in message.tool_calls:
            result = dispatch_tool(tool_call.function.name, 
                                   json.loads(tool_call.function.arguments))
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": str(result)
            })
    
    return "Max iterations reached"

result = run_agent("Build a web scraper for tech news and save results to a CSV file")

from openai import OpenAI
from pydantic import BaseModel
from typing import Optional

client = OpenAI()

class TechArticle(BaseModel):
    title: str
    summary: str
    key_technologies: list[str]
    difficulty_level: str  # beginner, intermediate, advanced
    estimated_read_time: int  # minutes
    code_examples_count: int
    prerequisites: list[str]

def extract_article_metadata(article_text: str) -> TechArticle:
    response = client.beta.chat.completions.parse(
        model="gpt-5",
        messages=[
            {
                "role": "user",
                "content": f"Extract structured metadata from this article:\n\n{article_text}"
            }
        ],
        response_format=TechArticle
    )
    
    return response.choices[0].message.parsed

from openai import OpenAI
from enum import Enum

client = OpenAI()

class TaskComplexity(Enum):
    SIMPLE = "simple"
    MODERATE = "moderate"
    COMPLEX = "complex"

def classify_task_complexity(task: str) -> TaskComplexity:
    """Use a cheap model to classify task complexity."""
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "user",
                "content": f"""Classify this task complexity as 'simple', 'moderate', or 'complex'.
                Only return the single word.
                
                Task: {task}"""
            }
        ]
    )
    
    result = response.choices[0].message.content.strip().lower()
    return TaskComplexity(result)

def smart_completion(task: str, content: str) -> str:
    """Route to appropriate model based on complexity."""
    complexity = classify_task_complexity(task)
    
    model_map = {
        TaskComplexity.SIMPLE: "gpt-4o-mini",
        TaskComplexity.MODERATE: "gpt-4o",
        TaskComplexity.COMPLEX: "gpt-5"
    }
    
    model = model_map[complexity]
    
    response = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "user", "content": f"{task}\n\n{content}"}
        ]
    )
    
    return response.choices[0].message.content

# System prompts longer than 1024 tokens are automatically cached
# Cache hit reduces cost by 50%
system_prompt = """
You are an expert software architect with 20+ years of experience...
[Very long system prompt that stays constant across requests]
"""

# First request - cache miss
response1 = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "Review this code: ..."}
    ]
)
print(f"Cached tokens: {response1.usage.prompt_tokens_details.cached_tokens}")

# Subsequent requests with same system prompt - cache hit!
response2 = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "Review this other code: ..."}
    ]
)
# 50% cheaper on the system prompt tokens

from openai import OpenAI
import json

client = OpenAI()

# Prepare training data
training_data = [
    {
        "messages": [
            {"role": "system", "content": "You are a specialized code reviewer for our company's Python standards."},
            {"role": "user", "content": "Review this code: def calc(x,y): return x+y"},
            {"role": "assistant", "content": "Issues found:\n1. Function name 'calc' is not descriptive\n2. Missing type hints\n3. Missing docstring\n\nRefactored:\n```python\ndef calculate_sum(x: float, y: float) -> float:\n    \"\"\"Calculate the sum of two numbers.\"\"\"\n    return x + y\n```"}
        ]
    }
    # ... more training examples
]

# Save training data
with open("training_data.jsonl", "w") as f:
    for item in training_data:
        f.write(json.dumps(item) + "\n")

# Upload and start fine-tuning
with open("training_data.jsonl", "rb") as f:
    file = client.files.create(file=f, purpose="fine-tune")

fine_tune_job = client.fine_tuning.jobs.create(
    training_file=file.id,
    model="gpt-5",
    hyperparameters={
        "n_epochs": 3,
        "batch_size": 8,
        "learning_rate_multiplier": 0.1
    }
)

print(f"Fine-tuning job ID: {fine_tune_job.id}")

def refactor_entire_codebase(repo_path: str, target_standard: str) -> dict:
    """Refactor an entire codebase using GPT-5's long context."""
    import os
    
    all_code = {}
    for root, _, files in os.walk(repo_path):
        for file in files:
            if file.endswith('.py'):
                filepath = os.path.join(root, file)
                with open(filepath) as f:
                    all_code[filepath] = f.read()
    
    combined = "\n\n".join([f"# File: {k}\n{v}" for k, v in all_code.items()])
    
    response = client.chat.completions.create(
        model="gpt-5",
        messages=[
            {
                "role": "user",
                "content": f"""Refactor this entire codebase to follow {target_standard} standards.
                Return a JSON object with filename as key and refactored code as value.
                
                {combined}"""
            }
        ]
    )
    
    return json.loads(response.choices[0].message.content)